HYBRID MACHINE LEARNING MODEL FOR EFFICIENT BOTNET ATTACK DETECTION IN IOT ENVIRONMENT

Abstract

The rapid growth of Internet of Things (IoT) devices has significantly increased connectivity and convenience in modern applications, but it has also introduced serious security vulnerabilities. IoT devices are often resource-constrained and lack robust security mechanisms, making them attractive targets for botnet attacks. Botnets can compromise large numbers of IoT devices and use them for malicious activities such as Distributed Denial of Service (DDoS) attacks, data theft, and unauthorized access. Detecting such attacks in IoT environments is a challenging task due to the dynamic nature of network traffic and the diversity of devices. This project proposes a hybrid machine learning model for efficient botnet attack detection in IoT environments. The proposed system combines multiple machine learning algorithms to improve detection accuracy and performance. It integrates techniques such as Decision Trees, Random Forests, and Deep Learning models to analyze network traffic and identify abnormal patterns associated with botnet activities. Feature extraction is performed on network data to capture relevant characteristics such as packet size, flow duration, and communication frequency. These features are then used to train the hybrid model, enabling it to distinguish between normal and malicious traffic. The hybrid approach leverages the strengths of different algorithms, providing better generalization and robustness compared to individual models. The system is trained and tested using publicly available IoT botnet datasets to ensure reliability and effectiveness. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the model. Overall, the proposed system provides a scalable and efficient solution for detecting botnet attacks in IoT environments. It enhances network security by enabling early detection and prevention of malicious activities, making it suitable for real-time monitoring and cybersecurity applications